Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning.
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ABSTRACT:
SUBMITTER: Li X
PROVIDER: S-EPMC8253978 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman's rho?=?0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho?=?0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual's tumour sample.
| S-EPMC5869697 | biostudies-literature
Project description:BackgroundThe response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.MethodsWe proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.FindingsFirst, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.InterpretationThese findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.FundingThis study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.
| S-EPMC8960981 | biostudies-literature
Project description:The alcohol-abuse drug disulfiram has antitumor effects against diverse cancer types via inhibition of the ubiquitin-proteasome protein nuclear protein localization protein 4 (NPL4). However, the antitumor effects of NPL4 and disulfiram in clear cell renal cell carcinoma (ccRCC) are unclear. Here, we evaluated the therapeutic potential of targeting the ubiquitin-proteasome pathway using disulfiram and RNA interference and investigated the mechanisms underlying disulfiram in ccRCC. According to data from The Cancer Genome Atlas, NPL4 mRNA expression was significantly upregulated in clinical ccRCC samples compared with that in normal kidney samples, and patients with high NPL4 expression had poor overall survival compared with patients with low NPL4 expression. Disulfiram and NPL4 siRNA inhibited ccRCC cell proliferation in vitro, and disulfiram inhibited ccRCC tumor growth in a xenograft model. Synergistic antiproliferative effects were observed for combination treatment with disulfiram and sunitinib in vitro and in vivo. In RCC cells from mice treated with disulfiram and/or sunitinib, several genes associated with serine biosynthesis and aldose reductase were downregulated in cells treated with disulfiram or sunitinib alone and further downregulated in cells treated with both disulfiram and sunitinib. These findings provided insights into the mechanisms of disulfiram and suggested novel therapeutic strategies for RCC treatment.
| S-EPMC7363112 | biostudies-literature
Project description:To find new potentially therapeutic drugs against clear cell Renal Cell Carcinoma (ccRCC), within drugs currently prescribed for other diseases (drug repositioning), we previously searched for drugs which are expected to bring the gene expression of 500?+?ccRCC samples from The Cancer Genome Atlas closer to that of healthy kidney tissue samples. An inherent limitation of this bulk RNA-seq data is that tumour samples consist of a varying mixture of cancerous and non-cancerous cells, which influences differential gene expression analyses. Here, we investigate whether the drug repositioning candidates are expected to target the genes dysregulated in ccRCC cells by studying the association with tumour purity. When all ccRCC samples are analysed together, the drug repositioning potential of identified drugs start decreasing above 80% estimated tumour purity. Because ccRCC is a highly vascular tumour, attributed to frequent loss of VHL function and subsequent activation of Hypoxia-Inducible Factor (HIF), we stratified the samples by observed activation of the HIF-pathway. After stratification, the association between estimated tumour purity and drug repositioning potential disappears for HIF-activated samples. This result suggests that the identified drug repositioning candidates specifically target the genes expressed by HIF-activated ccRCC tumour cells, instead of genes expressed by other cell types part of the tumour micro-environment.
| S-EPMC6385172 | biostudies-literature
Project description:Renal cell carcinomas (RCC) make up about 90% of kidney cancers, of which 80% are of the clear cell subtype. About 20% of patients are already metastatic at the time of diagnosis. Initial treatment is often cytoreductive nephrectomy, but systemic therapy is required for advanced RCC. Single agent targeted therapies are moderately toxic and only somewhat effective, leading to development of immunotherapies and combination therapies. This review identifies limitations of monotherapies for metastatic renal cell carcinoma, discusses recent advances in combination therapies, and highlights therapeutic options under development. The goal behind combining various modalities of systemic therapy is to potentiate a synergistic antitumor effect. However, combining targeted therapies may cause increased toxicity. The initial attempts to create therapeutic combinations based on inhibition of the vascular endothelial growth factor or mammalian target of rapamycin pathways were largely unsuccessful in achieving a profile of increased synergy without increased toxicity. To date, five combination therapies have been approved by the U.S. Food and Drug Administration, with the most recently approved therapies being a combination of checkpoint inhibition plus targeted therapy. Several other combination therapies are under development, including some in the phase 3 stage. The new wave of combination therapies for metastatic RCC has the potential to increase response rates and improve survival outcomes while maintaining tolerable side effect profiles.
| S-EPMC7443831 | biostudies-literature
Project description:The aim of our research is to explore the various characteristics and genetic profiles of clear cell renal cell carcinoma (ccRCC) in order to discover possible predictors of prognosis and targets for treatment. By utilizing ssGSEA scores, we categorized patients with ccRCC into groups based on their phenotype, distinguishing between low and high. This categorization revealed significant variations in the expression of crucial immune checkpoint genes and Human Leukocyte Antigen (HLA) genes, suggesting the presence of a potential immune evasion tactic in different subtypes of ccRCC. A predictive model was built using genes that are expressed differently and linked to cell death, showing strong effectiveness in categorizing patient risk. Furthermore, we discovered a noteworthy correlation among risk scores, infiltration of immune cells, the expression of genes related to immune checkpoint inhibitors, and diverse clinical features. This indicates that our scoring system for risk could function as a comprehensive gauge of the severity of the disease. The examination of the mutational terrain further highlighted the predominance of particular genetic changes, including VHL and PBRM1 missense mutations. Finally, we have discovered the function of DKK1 in facilitating cell death in ccRCC, presenting an additional possibility for therapeutic intervention. The results of our study suggest the possibility of incorporating molecular information into clinical prediction, which could lead to personalized treatment approaches in ccRCC.
| S-EPMC10577421 | biostudies-literature
Project description:Background/aimsFor metastatic renal cell carcinoma (RCC), various prognostic scoring systems have been developed. However, owing to the low prevalence of nonclear cell RCC, the three most commonly used tools were mainly developed based on patients with clear cell histology. Accordingly, this study applied three prognostic models to Korean non-clear cell RCC patients treated with first-line temsirolimus.MethodsThis study analyzed data for 74 patients with non-clear cell RCC who were treated with temsirolimus as the first-line therapy at eight medical centers between 2011 and 2016. The receiver-operating characteristic (ROC) curves for the different prognostic models were analyzed.ResultsTwenty-seven (36.5%), 24 (32.4%), and 44 patients (59.5%) were assigned to the poor prognosis groups of the Memorial Sloan-Kettering Cancer Center (MSKCC), International Metastatic RCC Database Consortium (IMDC), and Advanced Renal Cell Carcinoma (ARCC) risk stratification models, respectively. All three prognostic models reliably discriminated the risk groups to predict progression-free survival and overall survival (p < 0.001). The area under the ROC curve (AUC) for progression and survival was highest for the ARCC model (0.777; 0.734), followed by the IMDC (0.756; 0.724) and the MSKCC (0.742; 0.712) models. Furthermore, the sensitivity and specificity for predicting progression were highest with the ARCC model (sensitivity 63.6%, specificity 85.7%), followed by the MSKCC (sensitivity 58.2%, specificity 86.5%) and the IMDC models (sensitivity 56.4%, specificity 85.7%).ConclusionAll three prognostic models accurately predicted the survival of the non-clear cell RCC patients treated with temsirolimus as the first-line therapy. Furthermore, the ARCC risk model performed better than the other risk models in predicting survival.
| S-EPMC6960037 | biostudies-literature
Project description:Clear cell papillary renal cell carcinoma (CCPRCC) is a low-grade renal neoplasm with morphological characteristics mimicking both clear cell renal cell carcinoma (CCRCC) and papillary renal cell carcinoma (PRCC). However, despite some overlapping features, their morphological, immunohistochemical, and molecular profiles are distinct. Micro-RNAs (miRNAs) are small noncoding RNAs that play a crucial role in regulating gene expression and are involved in various biological processes, including cancer development. To better understand the biology of this tumor, we aimed to analyze the miRNA expression profile of a set of CCPRCC using microarray and quantitative reverse transcription-polymerase chain reaction. A total of 15 cases diagnosed as CCPRCC were used in this study. Among the most differentially expressed miRNA in CCPRCC, we found miR-210, miR-122, miR-34a, miR-21, miR-34b*, and miR-489 to be up-regulated, whereas miR-4284, miR-1202, miR-135a, miR-1973, and miR-204 were down-regulated compared with normal renal parenchyma. To identify consensus of differentially regulated miRNA between CCPRCC, CCRCC, and PRCC, we additionally determined differential miRNA expression using 2 publically available microarray data sets from the NCBI Gene Expression Omnibus database (GSE41282 and GSE3798). This comparison revealed that the miRNA expression profile of CCPRCC shows some overlapping characteristics between CCRCC and PRCC. Moreover, CCPRCC lacks dysregulation of important miRNAs typically associated with aggressive behavior. In summary, we describe the miRNA expression profile of a relatively infrequent type of renal cancer. Our results may help in understanding the molecular underpinning of this newly recognized entity.
| S-EPMC4332813 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.
| S-EPMC2943630 | biostudies-literature
Project description:Tumorigenic processes are understood to be driven by epi-/genetic and genomic alterations from single point mutations to chromosomal alterations such as insertions and deletions of nucleotides up to gains and losses of large chromosomal fragments including products of chromosomal rearrangements e.g. fusion genes and proteins. Overall comparisons of copy number alterations (CNAs) presented in 48 clear cell renal cell carcinoma (ccRCC) genomes resulted in ratios of gene losses versus gene gains between 26 ccRCC Fuhrman malignancy grades G1 (ratio 1.25) and 20 G3 (ratio 0.58). Gene losses and gains of 15762 CNA genes were mapped to 795 chromosomal cytoband loci including 280 KEGG pathways. CNAs were classified according to their contribution to Fuhrman tumour gradings G1 and G3. Gene gains and losses turned out to be highly structured processes in ccRCC genomes enabling the subclassification and stratification of ccRCC tumours in a genome-wide manner. CNAs of ccRCC seem to start with common tumour related gene losses flanked by CNAs specifying Fuhrman grade G1 losses and CNA gains favouring grade G3 tumours. The appearance of recurrent CNA signatures implies the presence of causal mechanisms most likely implicated in the pathogenesis and disease-outcome of ccRCC tumours distinguishing lower from higher malignant tumours. The diagnostic quality of initial 201 genes (108 genes supporting G1 and 93 genes G3 phenotypes) has been successfully validated on published Swiss data (GSE19949) leading to a restricted CNA gene set of 171 CNA genes of which 85 genes favour Fuhrman grade G1 and 86 genes Fuhrman grade G3. Regarding these gene sets overall survival decreased with the number of G3 related gene losses plus G3 related gene gains. CNA gene sets presented define an entry to a gene-directed and pathway-related functional understanding of ongoing copy number alterations within and between individual ccRCC tumours leading to CNA genes of prognostic and predictive value.
| S-EPMC5423597 | biostudies-literature